Metabolic pathway engineering has attracted significant interest in recent years catalyzed by the rapidly increasing number of sequenced microbial genes. As of January 2001, over fifty microbial genomes were completely sequenced. Bioinformatic tools have allowed the functional assignment of 45 to 80% of their coding regions. E. Pennisi, Science 277, 1432 (1997). This newly acquired information is used in conjunction with microbial mathematical models to calculate the response of metabolic networks after gene knockouts or additions. For example, such information was used to increase ethanol production in metabolically engineered E. coli cells. V. Hatzimanikatis, et al., Biotechnol. Bioeng. 58, 154 (1998).
In general, mathematical models of cellular metabolism fall into two distinct categories, ones that incorporate kinetic and regulatory information and others that include only the stoichiometry of the reaction pathways. The first class of models matches cellular behavior at an original steady state and then employs kinetic and regulatory relations to examine how the cell behaves away from this steady state in the presence of small perturbations brought about by environmental changes or enzyme engineering. The key advantage of this first class of methods is that upon application a unique point in the metabolite flux space is identified. The disadvantage is that the required kinetic parameters are difficult to estimate and their accuracy and reproducibility may deteriorate rapidly as the system moves far away from the original steady-state.
The second class of models, flux balance analyses, utilizes only the stoichiometric mass balances of the metabolic network and cellular composition information, in the absence of detailed kinetic and thermodynamic data, to identify boundaries for the flux distributions available to the cell. Although microorganisms have evolved highly complex control structures that eventually collapse these available boundaries into single points, flux balance models are still valuable in setting upper bounds for performance targets and in identifying “ideal” flux distributions.
However, the versatility of flux balance analysis comes at the expense of unknowingly crossing kinetic or regulatory flux barriers. Flux balance model predictions must thus be cautiously interpreted as “ideal” flux distributions yielding upper bounds to the performance of the metabolic network. The key advantage of flux balance models is that, by not requiring any numerical values for kinetic parameters or regulatory loops, they are straightforward to compile. The key disadvantage is that the obtained stoichiometric boundaries can be very wide and it is hard to envision that the biomass maximization conjecture, while useful under certain conditions, is generally applicable.
It is therefore a primary object of the present invention to provide a method and system that improves upon the state of the art.
It is a further object of the present invention to provide a method and system that provides a framework for improving upon flux balance analysis models.
It is a still further object of the present invention to provide a method and system that allows the predictive capabilities of flux balance analysis models to be enhanced.
Another object of the present invention is to provide a method and system that incorporates qualitative kinetic and/or regulatory information into a flux balance analysis model.
Yet another object of the present invention is to provide a method and system that incorporates differential DNA microarray experimental data into a flux balance analysis model.
A further object of the present invention is to provide an improved method and system for determining minimal reaction sets for growth.
Another object of the present invention is to provide an improved method and system for determining the effect of environmental conditions on minimal reaction sets.
It is another object of the present invention to provide a method for calculating the response of metabolic networks after gene knockouts or additions.
A still further object of the present invention is to provide a method and system for selecting mathematically optimal genes for recombination.
Another object of the present invention is to provide a method and system for identifying lethal gene deletions.
Yet another object of the present invention is to provide a method and system for identifying gene therapeutic candidates for pathogenic microbes.
A still further object of the present invention is to provide a method and system capable of testing hypotheses or objective functions.
These and other objects, features and/or advantages of the present invention will become apparent from the specification and claims.